To boost the search efficiency, many approximated nearest neighbor (ANN) search methods are proposed. The research on ANN dates back to at least the 1970s [1]}, [2]}. Traditionally, ANN search methods mainly include hashing-based methods [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, quantization-based method [11]}, [12]}, [13]}, [14]}, tree-based method [15]}, [16]}, [17]}, [18]}, and graph-based method [19]}, [20]}, [21]}, [22]}. In recent years, “neural ranking” has also attracted increasingly more attentions [23]}, [24]}, [25]}, [26]}, [27]}, [28]}, [29]}. Also, inspired by the success of deep learning, many deep Hashing [30]}, [31]} and deep PQ [32]}, [33]} work are proposed. Basically, them incorporate the Hashing and PQ in a neural network, and trains the Hashing or PQ codes in an end-to-end manner.
